CONSTRUCTION OF NOVEL NEURAL NETWORK MODEL BASED ON NEUROEVOLUTIONARY ALGORITHM FOR IMPROVING THE PERFORMANCE OF CROP YIELD PREDICTION

The development of machine learning combined with high speed computing power has opened up several multidisciplinary opportunities. Here, we provide a cutting-edge machine learning technique for forecasting agricultural production. The performance enhancement in agricultural yield prediction was proven by the classification approach utilising machine learning algorithm. It depends on the local soil, water irrigations, and meteorological variables that are related to data on climate change. Here, we’ve provided an example of how to use an ANN-based neuroevolution model to forecast wheat crop production. From June through September, crop yield projections are taken into account. The yields are generated based on climate and fertilizer usage statistics. The capacity to forecast how a season will evolve based on weather information has significantly improved. As a consequence, the model’s results help with decision-making before planting a wheat crop. The findings are more helpful for making decisions, planning wheat planting, and performing other agricultural duties at various stages of the wheat crop’s development. The same model may be utilised to predict a variety of agricultural data, such as disease and weather predictions. In this work, a neural evolution system for forecasting different plant illnesses was created. The performance increase in plant disease diagnosis may be proven using the algorithms of machine learning that support several categorization strategies. Disease prediction is based on local soil conditions and meteorological variables that are related to data on climate change. Here, we’ve shown how to use an ANN-based neuroevolution model to forecast a variety of plant illnesses. Different causes and the sort of illness that may harm various plants at various times of the year are anticipated. As a result, the results of the suggested model help with decision-making and preventative measures for plant diseases. The results may be used to ma